diff --git a/combo/config.graph.template.jsonnet b/combo/config.graph.template.jsonnet
index a4725606e3cbc1917c46966ee3bf38833de969bc..f33967002185adc02ae36ce9fbdb0724278226eb 100644
--- a/combo/config.graph.template.jsonnet
+++ b/combo/config.graph.template.jsonnet
@@ -274,25 +274,25 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
local projection_dim = 512,
cycle_loss_n: cycle_loss_n,
head_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768, #hidden_size * 2,
out_features: projection_dim,
activation: "tanh",
},
dependency_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768, #hidden_size * 2,
out_features: projection_dim,
activation: "tanh",
},
},
local projection_dim = 128,
head_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768,#hidden_size * 2,
out_features: projection_dim,
dropout_rate: predictors_dropout,
activation: "tanh"
},
dependency_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768, #hidden_size * 2,
out_features: projection_dim,
dropout_rate: predictors_dropout,
activation: "tanh"
@@ -305,25 +305,25 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
local projection_dim = 512,
cycle_loss_n: cycle_loss_n,
head_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768, #hidden_size * 2,
out_features: projection_dim,
activation: "tanh",
},
dependency_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768, #hidden_size * 2,
out_features: projection_dim,
activation: "tanh",
},
},
local projection_dim = 128,
head_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768, #hidden_size * 2,
out_features: projection_dim,
dropout_rate: predictors_dropout,
activation: "tanh"
},
dependency_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768, #hidden_size * 2,
out_features: projection_dim,
dropout_rate: predictors_dropout,
activation: "tanh"
@@ -332,7 +332,7 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
morphological_feat: if in_targets("feats") then {
type: "combo_morpho_from_vocab",
vocab_namespace: "feats_labels",
- input_dim: hidden_size * 2,
+ input_dim: char_dim + 768, #hidden_size * 2,
hidden_dims: [128],
activations: ["tanh", "linear"],
dropout: [predictors_dropout, 0.0],
@@ -344,7 +344,7 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
lemma_vocab_namespace: "lemma_characters",
embedding_dim: 256,
input_projection_layer: {
- in_features: hidden_size * 2,
+ in_features: char_dim + 768, #hidden_size * 2,
out_features: 32,
dropout_rate: predictors_dropout,
activation: "tanh"
@@ -357,7 +357,7 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
activations: ["relu", "relu", "relu", "linear"],
},
upos_tagger: if in_targets("upostag") then {
- input_dim: hidden_size * 2,
+ input_dim: char_dim + 768, #hidden_size * 2,
hidden_dims: [64],
activations: ["tanh", "linear"],
dropout: [predictors_dropout, 0.0],
@@ -365,7 +365,7 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
vocab_namespace: "upostag_labels"
},
xpos_tagger: if in_targets("xpostag") then {
- input_dim: hidden_size * 2,
+ input_dim: char_dim + 768, #hidden_size * 2,
hidden_dims: [128],
activations: ["tanh", "linear"],
dropout: [predictors_dropout, 0.0],
@@ -373,7 +373,7 @@ assert pretrained_tokens == null || pretrained_transformer_name == null: "Can't
vocab_namespace: "xpostag_labels"
},
semantic_relation: if in_targets("semrel") then {
- input_dim: hidden_size * 2,
+ input_dim: char_dim + 768, #hidden_size * 2,
hidden_dims: [64],
activations: ["tanh", "linear"],
dropout: [predictors_dropout, 0.0],
diff --git a/combo/training/scheduler.py b/combo/training/scheduler.py
index 8752d739029f16289db94ee493d00bbd92158ce7..b749a55467bece95153d0a0621dc92de23165fa0 100644
--- a/combo/training/scheduler.py
+++ b/combo/training/scheduler.py
@@ -7,7 +7,7 @@ from overrides import overrides
class Scheduler(learning_rate_scheduler._PyTorchLearningRateSchedulerWrapper):
def __init__(self, optimizer, patience: int = 6, decreases: int = 2, threshold: float = 1e-3):
- super().__init__(lr_scheduler.LambdaLR(optimizer, lr_lambda=[self._lr_lambda]))
+ super().__init__(lr_scheduler.LambdaLR(optimizer, lr_lambda=[self._lr_lambda,self._lr_lambda]))
self.threshold = threshold
self.decreases = decreases
self.patience = patience
diff --git a/combo/training/trainer.py b/combo/training/trainer.py
index 3bee8fcaaf1c29189583a551a1100ac4f0215a65..ae4c0d04a3b7c762ac5cb9302d961214d4b54167 100644
--- a/combo/training/trainer.py
+++ b/combo/training/trainer.py
@@ -32,11 +32,69 @@ class TransferPatienceEpochCallback(training.EpochCallback):
def __call__(self, trainer: "training.GradientDescentTrainer", metrics: Dict[str, Any], epoch: int,
is_master: bool) -> None:
- if trainer._learning_rate_scheduler and trainer._learning_rate_scheduler.patience is not None:
- trainer._metric_tracker._patience = trainer._learning_rate_scheduler.patience
- trainer._metric_tracker._epochs_with_no_improvement = 0
+
+ #LR range test variables
+ path_to_result_file = "/tmp/lustre_shared/lukasz/tmp/LR_range_3.txt"
+ end_lr = 0.001
+ start_lr = 0.0000001
+ num_lr_in_test = 25
+ lr_update_factor = (end_lr / start_lr) ** (1.0 / num_lr_in_test)
+ # # # # # # #
+
+ param_group = trainer.optimizer.param_groups
+
+ if epoch == 0:
+ with open(path_to_result_file, "a") as file:
+ file.write("\n" + str(param_group[0]["lr"]) + ";" + str(param_group[1]["lr"]) + ";")
else:
- raise checks.ConfigurationError("Learning rate scheduler isn't properly setup!")
+ with open(path_to_result_file, "a") as file:
+ file.write(str(metrics["training_loss"]) + ";" +
+ str(metrics["validation_loss"]) + ";" +
+ str(metrics["best_epoch"]) + ";" +
+ #training losses
+ str(metrics["training_partial_loss/upostag_loss"]) + ";" +
+ str(metrics["training_partial_loss/xpostag_loss"]) + ";" +
+ str(metrics["training_partial_loss/feats_loss"]) + ";" +
+ str(metrics["training_partial_loss/lemma_loss"]) + ";" +
+ str(metrics["training_partial_loss/head_loss"]) + ";" +
+ str(metrics["training_partial_loss/deprel_loss"]) + ";" +
+ #training acc
+ str(metrics["training_UPOS_ACC"]) + ";" +
+ str(metrics["training_XPOS_ACC"]) + ";" +
+ str(metrics["training_SEMREL_ACC"]) + ";" +
+ str(metrics["training_LEMMA_ACC"]) + ";" +
+ str(metrics["training_FEATS_ACC"]) + ";" +
+ str(metrics["training_LAS"]) + ";" +
+ #validation losses
+ str(metrics["validation_partial_loss/upostag_loss"]) + ";" +
+ str(metrics["validation_partial_loss/xpostag_loss"]) + ";" +
+ str(metrics["validation_partial_loss/feats_loss"]) + ";" +
+ str(metrics["validation_partial_loss/lemma_loss"]) + ";" +
+ str(metrics["validation_partial_loss/head_loss"]) + ";" +
+ str(metrics["validation_partial_loss/deprel_loss"]) + ";" +
+ # validation acc
+ str(metrics["validation_UPOS_ACC"]) + ";" +
+ str(metrics["validation_XPOS_ACC"]) + ";" +
+ str(metrics["validation_SEMREL_ACC"]) + ";" +
+ str(metrics["validation_LEMMA_ACC"]) + ";" +
+ str(metrics["validation_FEATS_ACC"]) + ";" +
+ str(metrics["validation_LAS"]) + ";" +
+ # best loss
+ str(metrics["best_validation_loss"]) + ";"
+ )
+
+ # END CONDITIONS
+ if param_group[1]["lr"] >= end_lr and param_group[0]["lr"] >= end_lr:
+ raise Exception('End of LR test')
+
+ param_group[0]["lr"] = param_group[0]["lr"] * lr_update_factor
+ if param_group[0]["lr"] >= end_lr:
+ param_group[0]["lr"] = start_lr
+ param_group[1]["lr"] = param_group[1]["lr"] * lr_update_factor
+
+ file.write("\n" + str(param_group[0]["lr"]) + ";" + str(param_group[1]["lr"]) + ";")
+
+ trainer.optimizer.param_groups = param_group
@training.Trainer.register("gradient_descent_validate_n", constructor="from_partial_objects")
@@ -65,7 +123,7 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
batch_callbacks, epoch_callbacks, end_callbacks, trainer_callbacks, distributed, local_rank, world_size,
num_gradient_accumulation_steps, use_amp)
# TODO extract param to constructor (+ constructor method?)
- self.validate_every_n = 5
+ self.validate_every_n = 1
@overrides
def _try_train(self) -> Dict[str, Any]:
@@ -93,26 +151,16 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
for key, value in self._metric_tracker.best_epoch_metrics.items():
metrics["best_validation_" + key] = value
- for callback in self._epoch_callbacks:
- callback(self, metrics={}, epoch=-1, is_master=self._master)
-
for epoch in range(epoch_counter, self._num_epochs):
- epoch_start_time = time.time()
- train_metrics = self._train_epoch(epoch)
- if self._master and self._checkpointer is not None:
- self._checkpointer.save_checkpoint(epoch, self, save_model_only=True)
+ epochs_to_change_lr = 15
- # Wait for the master to finish saving the model checkpoint
- if self._distributed:
- dist.barrier()
+ # every epochs_to_change_lr epoch loads weights after 1 epoch
+ if (epoch - 1) % epochs_to_change_lr == 0 and epoch > 1:
+ self.model.load_state_dict(torch.load(os.path.join(self._serialization_dir, "initial.th")))
- # get peak of memory usage
- for key, value in train_metrics.items():
- if key.startswith("gpu_") and key.endswith("_memory_MB"):
- metrics["peak_" + key] = max(metrics.get("peak_" + key, 0), value)
- elif key.startswith("worker_") and key.endswith("_memory_MB"):
- metrics["peak_" + key] = max(metrics.get("peak_" + key, 0), value)
+ epoch_start_time = time.time()
+ train_metrics = self._train_epoch(epoch)
if self._validation_data_loader is not None:
val_metrics = {}
@@ -141,24 +189,6 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
# Check validation metric for early stopping
this_epoch_val_metric = val_metrics[self._validation_metric]
- # self._metric_tracker.add_metric(this_epoch_val_metric)
-
- train_metrics["patience"] = self._metric_tracker._patience
- if self._metric_tracker.should_stop_early():
- logger.info("Ran out of patience. Stopping training.")
- break
-
- if self._master:
- self._tensorboard.log_metrics(
- train_metrics, val_metrics=val_metrics, log_to_console=True, epoch=epoch + 1
- ) # +1 because tensorboard doesn't like 0
-
- # Create overall metrics dict
- training_elapsed_time = time.time() - training_start_time
- metrics["training_duration"] = str(datetime.timedelta(seconds=training_elapsed_time))
- metrics["training_start_epoch"] = epoch_counter
- metrics["training_epochs"] = epochs_trained
- metrics["epoch"] = epoch
for key, value in train_metrics.items():
metrics["training_" + key] = value
@@ -174,10 +204,9 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
self._metric_tracker.best_epoch_metrics = val_metrics
- if self._serialization_dir and self._master:
- common_util.dump_metrics(
- os.path.join(self._serialization_dir, f"metrics_epoch_{epoch}.json"), metrics
- )
+ for callback in self._epoch_callbacks:
+ if ((epoch-1) % epochs_to_change_lr == 0 and epoch > 1 ) or epoch == 0:
+ callback(self, metrics=metrics, epoch=epoch, is_master=self._master)
# The Scheduler API is agnostic to whether your schedule requires a validation metric -
# if it doesn't, the validation metric passed here is ignored.
@@ -186,18 +215,6 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
if self._momentum_scheduler:
self._momentum_scheduler.step(this_epoch_val_metric)
- if self._master and self._checkpointer is not None:
- self._checkpointer.save_checkpoint(
- epoch, self, is_best_so_far=self._metric_tracker.is_best_so_far()
- )
-
- # Wait for the master to finish saving the checkpoint
- if self._distributed:
- dist.barrier()
-
- for callback in self._epoch_callbacks:
- callback(self, metrics=metrics, epoch=epoch, is_master=self._master)
-
epoch_elapsed_time = time.time() - epoch_start_time
logger.info("Epoch duration: %s", datetime.timedelta(seconds=epoch_elapsed_time))
@@ -211,16 +228,15 @@ class GradientDescentTrainer(training.GradientDescentTrainer):
epochs_trained += 1
+ if epoch == 0:
+ torch.save(self.model.state_dict(), os.path.join(self._serialization_dir, "initial.th"))
+
+ if (epoch-1) % epochs_to_change_lr == 0 and epoch > 1:
+ self._metric_tracker.best_epoch_metrics = val_metrics
+
for callback in self._end_callbacks:
callback(self, metrics=metrics, epoch=epoch, is_master=self._master)
- # Load the best model state before returning
- best_model_state = (
- None if self._checkpointer is None else self._checkpointer.best_model_state()
- )
- if best_model_state:
- self.model.load_state_dict(best_model_state)
-
return metrics
@classmethod